Overview

Dataset statistics

Number of variables26
Number of observations15
Missing cells46
Missing cells (%)11.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.2 KiB
Average record size in memory216.5 B

Variable types

Categorical4
Numeric22

Alerts

number_of_countries is highly correlated with aminoglycosides_tonnes and 15 other fieldsHigh correlation
aminoglycosides_tonnes is highly correlated with number_of_countries and 14 other fieldsHigh correlation
amphenicols_tonnes is highly correlated with number_of_countries and 15 other fieldsHigh correlation
arsenicals_tonnes is highly correlated with macrolides_tonnes and 5 other fieldsHigh correlation
cephalosporins__all_generations_tonnes is highly correlated with number_of_countries and 16 other fieldsHigh correlation
1_2_gen__cephalosporins_tonnes is highly correlated with number_of_countries and 17 other fieldsHigh correlation
3_4_gen_cephalosporins_tonnes is highly correlated with number_of_countries and 17 other fieldsHigh correlation
fluoroquinolones_tonnes is highly correlated with number_of_countries and 15 other fieldsHigh correlation
glycophospholipids_tonnes is highly correlated with nitrofurans_tonnesHigh correlation
lincosamides_tonnes is highly correlated with number_of_countries and 17 other fieldsHigh correlation
macrolides_tonnes is highly correlated with number_of_countries and 19 other fieldsHigh correlation
nitrofurans_tonnes is highly correlated with number_of_countries and 10 other fieldsHigh correlation
orthosomycins_tonnes is highly correlated with arsenicals_tonnes and 12 other fieldsHigh correlation
other_quinolones_tonnes is highly correlated with number_of_countries and 14 other fieldsHigh correlation
penicillins_tonnes is highly correlated with number_of_countries and 16 other fieldsHigh correlation
pleuromutilins_tonnes is highly correlated with number_of_countries and 17 other fieldsHigh correlation
polypeptides_tonnes is highly correlated with number_of_countries and 14 other fieldsHigh correlation
quinoxalines_tonnes is highly correlated with arsenicals_tonnes and 5 other fieldsHigh correlation
streptogramins_tonnes is highly correlated with arsenicals_tonnes and 9 other fieldsHigh correlation
sulfonamides__including_trimethoprim_tonnes is highly correlated with number_of_countries and 15 other fieldsHigh correlation
tetracyclines_tonnes is highly correlated with number_of_countries and 17 other fieldsHigh correlation
others_tonnes is highly correlated with number_of_countries and 16 other fieldsHigh correlation
aggregated_class_data_tonnes is highly correlated with amphenicols_tonnes and 12 other fieldsHigh correlation
number_of_countries is highly correlated with lincosamides_tonnes and 1 other fieldsHigh correlation
aminoglycosides_tonnes is highly correlated with amphenicols_tonnes and 15 other fieldsHigh correlation
amphenicols_tonnes is highly correlated with aminoglycosides_tonnes and 14 other fieldsHigh correlation
arsenicals_tonnes is highly correlated with 1_2_gen__cephalosporins_tonnes and 10 other fieldsHigh correlation
cephalosporins__all_generations_tonnes is highly correlated with aminoglycosides_tonnes and 14 other fieldsHigh correlation
1_2_gen__cephalosporins_tonnes is highly correlated with aminoglycosides_tonnes and 10 other fieldsHigh correlation
3_4_gen_cephalosporins_tonnes is highly correlated with aminoglycosides_tonnes and 13 other fieldsHigh correlation
fluoroquinolones_tonnes is highly correlated with aminoglycosides_tonnes and 16 other fieldsHigh correlation
glycophospholipids_tonnes is highly correlated with quinoxalines_tonnes and 2 other fieldsHigh correlation
lincosamides_tonnes is highly correlated with number_of_countries and 16 other fieldsHigh correlation
macrolides_tonnes is highly correlated with aminoglycosides_tonnes and 17 other fieldsHigh correlation
nitrofurans_tonnes is highly correlated with aminoglycosides_tonnes and 17 other fieldsHigh correlation
orthosomycins_tonnes is highly correlated with aminoglycosides_tonnes and 17 other fieldsHigh correlation
other_quinolones_tonnes is highly correlated with number_of_countries and 16 other fieldsHigh correlation
penicillins_tonnes is highly correlated with aminoglycosides_tonnes and 15 other fieldsHigh correlation
pleuromutilins_tonnes is highly correlated with aminoglycosides_tonnes and 18 other fieldsHigh correlation
polypeptides_tonnes is highly correlated with aminoglycosides_tonnes and 17 other fieldsHigh correlation
quinoxalines_tonnes is highly correlated with arsenicals_tonnes and 8 other fieldsHigh correlation
streptogramins_tonnes is highly correlated with arsenicals_tonnes and 8 other fieldsHigh correlation
sulfonamides__including_trimethoprim_tonnes is highly correlated with aminoglycosides_tonnes and 15 other fieldsHigh correlation
tetracyclines_tonnes is highly correlated with aminoglycosides_tonnes and 18 other fieldsHigh correlation
others_tonnes is highly correlated with aminoglycosides_tonnes and 17 other fieldsHigh correlation
aggregated_class_data_tonnes is highly correlated with 1_2_gen__cephalosporins_tonnes and 1 other fieldsHigh correlation
number_of_countries is highly correlated with aminoglycosides_tonnes and 12 other fieldsHigh correlation
aminoglycosides_tonnes is highly correlated with number_of_countries and 12 other fieldsHigh correlation
amphenicols_tonnes is highly correlated with number_of_countries and 14 other fieldsHigh correlation
arsenicals_tonnes is highly correlated with macrolides_tonnes and 5 other fieldsHigh correlation
cephalosporins__all_generations_tonnes is highly correlated with number_of_countries and 14 other fieldsHigh correlation
1_2_gen__cephalosporins_tonnes is highly correlated with number_of_countries and 14 other fieldsHigh correlation
3_4_gen_cephalosporins_tonnes is highly correlated with number_of_countries and 15 other fieldsHigh correlation
fluoroquinolones_tonnes is highly correlated with number_of_countries and 14 other fieldsHigh correlation
lincosamides_tonnes is highly correlated with number_of_countries and 17 other fieldsHigh correlation
macrolides_tonnes is highly correlated with number_of_countries and 16 other fieldsHigh correlation
nitrofurans_tonnes is highly correlated with number_of_countries and 3 other fieldsHigh correlation
orthosomycins_tonnes is highly correlated with arsenicals_tonnes and 9 other fieldsHigh correlation
other_quinolones_tonnes is highly correlated with number_of_countries and 11 other fieldsHigh correlation
penicillins_tonnes is highly correlated with number_of_countries and 13 other fieldsHigh correlation
pleuromutilins_tonnes is highly correlated with number_of_countries and 16 other fieldsHigh correlation
polypeptides_tonnes is highly correlated with arsenicals_tonnes and 11 other fieldsHigh correlation
quinoxalines_tonnes is highly correlated with arsenicals_tonnes and 5 other fieldsHigh correlation
streptogramins_tonnes is highly correlated with arsenicals_tonnes and 7 other fieldsHigh correlation
sulfonamides__including_trimethoprim_tonnes is highly correlated with aminoglycosides_tonnes and 12 other fieldsHigh correlation
tetracyclines_tonnes is highly correlated with number_of_countries and 17 other fieldsHigh correlation
others_tonnes is highly correlated with amphenicols_tonnes and 10 other fieldsHigh correlation
aggregated_class_data_tonnes is highly correlated with cephalosporins__all_generations_tonnes and 7 other fieldsHigh correlation
scope is highly correlated with number_of_countriesHigh correlation
region is highly correlated with arsenicals_tonnes and 4 other fieldsHigh correlation
number_of_countries is highly correlated with scope and 20 other fieldsHigh correlation
aminoglycosides_tonnes is highly correlated with number_of_countries and 20 other fieldsHigh correlation
amphenicols_tonnes is highly correlated with number_of_countries and 18 other fieldsHigh correlation
arsenicals_tonnes is highly correlated with region and 11 other fieldsHigh correlation
cephalosporins__all_generations_tonnes is highly correlated with number_of_countries and 18 other fieldsHigh correlation
1_2_gen__cephalosporins_tonnes is highly correlated with number_of_countries and 17 other fieldsHigh correlation
3_4_gen_cephalosporins_tonnes is highly correlated with region and 14 other fieldsHigh correlation
fluoroquinolones_tonnes is highly correlated with number_of_countries and 20 other fieldsHigh correlation
glycopeptides_tonnes is highly correlated with region and 2 other fieldsHigh correlation
glycophospholipids_tonnes is highly correlated with number_of_countries and 14 other fieldsHigh correlation
lincosamides_tonnes is highly correlated with number_of_countries and 18 other fieldsHigh correlation
macrolides_tonnes is highly correlated with number_of_countries and 21 other fieldsHigh correlation
nitrofurans_tonnes is highly correlated with number_of_countries and 16 other fieldsHigh correlation
orthosomycins_tonnes is highly correlated with number_of_countries and 19 other fieldsHigh correlation
other_quinolones_tonnes is highly correlated with number_of_countries and 17 other fieldsHigh correlation
penicillins_tonnes is highly correlated with number_of_countries and 19 other fieldsHigh correlation
pleuromutilins_tonnes is highly correlated with number_of_countries and 18 other fieldsHigh correlation
polypeptides_tonnes is highly correlated with number_of_countries and 21 other fieldsHigh correlation
quinoxalines_tonnes is highly correlated with region and 11 other fieldsHigh correlation
streptogramins_tonnes is highly correlated with region and 11 other fieldsHigh correlation
sulfonamides__including_trimethoprim_tonnes is highly correlated with number_of_countries and 18 other fieldsHigh correlation
tetracyclines_tonnes is highly correlated with number_of_countries and 21 other fieldsHigh correlation
others_tonnes is highly correlated with number_of_countries and 21 other fieldsHigh correlation
aggregated_class_data_tonnes is highly correlated with number_of_countries and 15 other fieldsHigh correlation
aminoglycosides_tonnes has 2 (13.3%) missing values Missing
amphenicols_tonnes has 2 (13.3%) missing values Missing
arsenicals_tonnes has 2 (13.3%) missing values Missing
cephalosporins__all_generations_tonnes has 2 (13.3%) missing values Missing
1_2_gen__cephalosporins_tonnes has 2 (13.3%) missing values Missing
3_4_gen_cephalosporins_tonnes has 2 (13.3%) missing values Missing
fluoroquinolones_tonnes has 2 (13.3%) missing values Missing
glycopeptides_tonnes has 2 (13.3%) missing values Missing
glycophospholipids_tonnes has 2 (13.3%) missing values Missing
lincosamides_tonnes has 2 (13.3%) missing values Missing
macrolides_tonnes has 2 (13.3%) missing values Missing
nitrofurans_tonnes has 2 (13.3%) missing values Missing
orthosomycins_tonnes has 2 (13.3%) missing values Missing
other_quinolones_tonnes has 2 (13.3%) missing values Missing
penicillins_tonnes has 2 (13.3%) missing values Missing
pleuromutilins_tonnes has 2 (13.3%) missing values Missing
polypeptides_tonnes has 2 (13.3%) missing values Missing
quinoxalines_tonnes has 2 (13.3%) missing values Missing
streptogramins_tonnes has 2 (13.3%) missing values Missing
sulfonamides__including_trimethoprim_tonnes has 2 (13.3%) missing values Missing
tetracyclines_tonnes has 2 (13.3%) missing values Missing
others_tonnes has 2 (13.3%) missing values Missing
aggregated_class_data_tonnes has 2 (13.3%) missing values Missing
scope is uniformly distributed Uniform
region is uniformly distributed Uniform
number_of_countries has 2 (13.3%) zeros Zeros
aminoglycosides_tonnes has 2 (13.3%) zeros Zeros
amphenicols_tonnes has 3 (20.0%) zeros Zeros
cephalosporins__all_generations_tonnes has 4 (26.7%) zeros Zeros
1_2_gen__cephalosporins_tonnes has 5 (33.3%) zeros Zeros
3_4_gen_cephalosporins_tonnes has 5 (33.3%) zeros Zeros
fluoroquinolones_tonnes has 3 (20.0%) zeros Zeros
glycophospholipids_tonnes has 6 (40.0%) zeros Zeros
lincosamides_tonnes has 2 (13.3%) zeros Zeros
macrolides_tonnes has 1 (6.7%) zeros Zeros
nitrofurans_tonnes has 6 (40.0%) zeros Zeros
orthosomycins_tonnes has 6 (40.0%) zeros Zeros
other_quinolones_tonnes has 5 (33.3%) zeros Zeros
penicillins_tonnes has 1 (6.7%) zeros Zeros
pleuromutilins_tonnes has 4 (26.7%) zeros Zeros
polypeptides_tonnes has 1 (6.7%) zeros Zeros
quinoxalines_tonnes has 8 (53.3%) zeros Zeros
streptogramins_tonnes has 4 (26.7%) zeros Zeros
sulfonamides__including_trimethoprim_tonnes has 1 (6.7%) zeros Zeros
others_tonnes has 3 (20.0%) zeros Zeros
aggregated_class_data_tonnes has 4 (26.7%) zeros Zeros

Reproduction

Analysis started2023-01-19 20:08:30.208264
Analysis finished2023-01-19 20:10:10.927923
Duration1 minute and 40.72 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

scope
Categorical

HIGH CORRELATION
UNIFORM

Distinct3
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size248.0 B
AGP
All
Terrestrial Food Producing

Length

Max length26
Median length3
Mean length10.66666667
Min length3

Characters and Unicode

Total characters160
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAGP
2nd rowAGP
3rd rowAGP
4th rowAGP
5th rowAGP

Common Values

ValueCountFrequency (%)
AGP5
33.3%
All5
33.3%
Terrestrial Food Producing5
33.3%

Length

2023-01-19T12:10:11.003242image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-19T12:10:11.170714image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
agp5
20.0%
all5
20.0%
terrestrial5
20.0%
food5
20.0%
producing5
20.0%

Most occurring characters

ValueCountFrequency (%)
r20
12.5%
l15
 
9.4%
o15
 
9.4%
A10
 
6.2%
d10
 
6.2%
10
 
6.2%
i10
 
6.2%
e10
 
6.2%
P10
 
6.2%
s5
 
3.1%
Other values (9)45
28.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter115
71.9%
Uppercase Letter35
 
21.9%
Space Separator10
 
6.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r20
17.4%
l15
13.0%
o15
13.0%
d10
8.7%
i10
8.7%
e10
8.7%
s5
 
4.3%
t5
 
4.3%
a5
 
4.3%
u5
 
4.3%
Other values (3)15
13.0%
Uppercase Letter
ValueCountFrequency (%)
A10
28.6%
P10
28.6%
G5
14.3%
F5
14.3%
T5
14.3%
Space Separator
ValueCountFrequency (%)
10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin150
93.8%
Common10
 
6.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
r20
13.3%
l15
 
10.0%
o15
 
10.0%
A10
 
6.7%
d10
 
6.7%
i10
 
6.7%
e10
 
6.7%
P10
 
6.7%
s5
 
3.3%
t5
 
3.3%
Other values (8)40
26.7%
Common
ValueCountFrequency (%)
10
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII160
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r20
12.5%
l15
 
9.4%
o15
 
9.4%
A10
 
6.2%
d10
 
6.2%
10
 
6.2%
i10
 
6.2%
e10
 
6.2%
P10
 
6.2%
s5
 
3.1%
Other values (9)45
28.1%

region
Categorical

HIGH CORRELATION
UNIFORM

Distinct5
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Memory size248.0 B
Africa
Americas
Asia, Far East and Oceania
Europe
Middle East

Length

Max length26
Median length11
Mean length11.4
Min length6

Characters and Unicode

Total characters171
Distinct characters22
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAfrica
2nd rowAmericas
3rd rowAsia, Far East and Oceania
4th rowEurope
5th rowMiddle East

Common Values

ValueCountFrequency (%)
Africa3
20.0%
Americas3
20.0%
Asia, Far East and Oceania3
20.0%
Europe3
20.0%
Middle East3
20.0%

Length

2023-01-19T12:10:11.334713image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-19T12:10:11.548860image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
east6
20.0%
africa3
10.0%
americas3
10.0%
asia3
10.0%
far3
10.0%
and3
10.0%
oceania3
10.0%
europe3
10.0%
middle3
10.0%

Most occurring characters

ValueCountFrequency (%)
a27
15.8%
i15
 
8.8%
15
 
8.8%
r12
 
7.0%
e12
 
7.0%
s12
 
7.0%
A9
 
5.3%
c9
 
5.3%
E9
 
5.3%
d9
 
5.3%
Other values (12)42
24.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter126
73.7%
Uppercase Letter27
 
15.8%
Space Separator15
 
8.8%
Other Punctuation3
 
1.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a27
21.4%
i15
11.9%
r12
9.5%
e12
9.5%
s12
9.5%
c9
 
7.1%
d9
 
7.1%
t6
 
4.8%
n6
 
4.8%
u3
 
2.4%
Other values (5)15
11.9%
Uppercase Letter
ValueCountFrequency (%)
A9
33.3%
E9
33.3%
M3
 
11.1%
F3
 
11.1%
O3
 
11.1%
Space Separator
ValueCountFrequency (%)
15
100.0%
Other Punctuation
ValueCountFrequency (%)
,3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin153
89.5%
Common18
 
10.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a27
17.6%
i15
9.8%
r12
 
7.8%
e12
 
7.8%
s12
 
7.8%
A9
 
5.9%
c9
 
5.9%
E9
 
5.9%
d9
 
5.9%
t6
 
3.9%
Other values (10)33
21.6%
Common
ValueCountFrequency (%)
15
83.3%
,3
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII171
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a27
15.8%
i15
 
8.8%
15
 
8.8%
r12
 
7.0%
e12
 
7.0%
s12
 
7.0%
A9
 
5.3%
c9
 
5.3%
E9
 
5.3%
d9
 
5.3%
Other values (12)42
24.6%

number_of_countries
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct12
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.53333333
Minimum0
Maximum41
Zeros2
Zeros (%)13.3%
Negative0
Negative (%)0.0%
Memory size248.0 B
2023-01-19T12:10:11.673059image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median6
Q315
95-th percentile29.1
Maximum41
Range41
Interquartile range (IQR)13

Descriptive statistics

Standard deviation11.48829632
Coefficient of variation (CV)1.090661043
Kurtosis2.306322695
Mean10.53333333
Median Absolute Deviation (MAD)5
Skewness1.520864849
Sum158
Variance131.9809524
MonotonicityNot monotonic
2023-01-19T12:10:11.813239image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
12
13.3%
62
13.3%
02
13.3%
51
6.7%
241
6.7%
191
6.7%
221
6.7%
411
6.7%
31
6.7%
111
6.7%
Other values (2)2
13.3%
ValueCountFrequency (%)
02
13.3%
12
13.3%
31
6.7%
51
6.7%
62
13.3%
91
6.7%
101
6.7%
111
6.7%
191
6.7%
221
6.7%
ValueCountFrequency (%)
411
6.7%
241
6.7%
221
6.7%
191
6.7%
111
6.7%
101
6.7%
91
6.7%
62
13.3%
51
6.7%
31
6.7%

aminoglycosides_tonnes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct12
Distinct (%)92.3%
Missing2
Missing (%)13.3%
Infinite0
Infinite (%)0.0%
Mean324.6236978
Minimum0
Maximum1425.663333
Zeros2
Zeros (%)13.3%
Negative0
Negative (%)0.0%
Memory size248.0 B
2023-01-19T12:10:11.946696image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.97
median33.65154354
Q3527.6433177
95-th percentile1296.684861
Maximum1425.663333
Range1425.663333
Interquartile range (IQR)524.6733177

Descriptive statistics

Standard deviation501.9562068
Coefficient of variation (CV)1.546270991
Kurtosis0.8525411644
Mean324.6236978
Median Absolute Deviation (MAD)33.65154354
Skewness1.462623508
Sum4220.108071
Variance251960.0335
MonotonicityNot monotonic
2023-01-19T12:10:12.064705image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
02
13.3%
0.7071
6.7%
33.651543541
6.7%
762.37578261
6.7%
1425.6633331
6.7%
527.64331771
6.7%
4.6511
6.7%
3.80168551
6.7%
166.50740411
6.7%
1210.6992131
6.7%
Other values (2)2
13.3%
(Missing)2
13.3%
ValueCountFrequency (%)
02
13.3%
0.7071
6.7%
2.971
6.7%
3.80168551
6.7%
4.6511
6.7%
33.651543541
6.7%
81.437791991
6.7%
166.50740411
6.7%
527.64331771
6.7%
762.37578261
6.7%
ValueCountFrequency (%)
1425.6633331
6.7%
1210.6992131
6.7%
762.37578261
6.7%
527.64331771
6.7%
166.50740411
6.7%
81.437791991
6.7%
33.651543541
6.7%
4.6511
6.7%
3.80168551
6.7%
2.971
6.7%

amphenicols_tonnes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct11
Distinct (%)84.6%
Missing2
Missing (%)13.3%
Infinite0
Infinite (%)0.0%
Mean438.8205738
Minimum0
Maximum2422.624924
Zeros3
Zeros (%)20.0%
Negative0
Negative (%)0.0%
Memory size248.0 B
2023-01-19T12:10:12.216701image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.5
median20.41030729
Q3243.8731763
95-th percentile2253.931439
Maximum2422.624924
Range2422.624924
Interquartile range (IQR)242.3731763

Descriptive statistics

Standard deviation845.0328321
Coefficient of variation (CV)1.925691006
Kurtosis2.614525618
Mean438.8205738
Median Absolute Deviation (MAD)20.41030729
Skewness1.971631879
Sum5704.667459
Variance714080.4873
MonotonicityNot monotonic
2023-01-19T12:10:12.342776image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
03
20.0%
20.410307291
 
6.7%
739.7979251
 
6.7%
2422.6249241
 
6.7%
243.87317631
 
6.7%
1.61
 
6.7%
9.35481
 
6.7%
96.027343781
 
6.7%
2141.4691151
 
6.7%
28.00986751
 
6.7%
(Missing)2
13.3%
ValueCountFrequency (%)
03
20.0%
1.51
 
6.7%
1.61
 
6.7%
9.35481
 
6.7%
20.410307291
 
6.7%
28.00986751
 
6.7%
96.027343781
 
6.7%
243.87317631
 
6.7%
739.7979251
 
6.7%
2141.4691151
 
6.7%
ValueCountFrequency (%)
2422.6249241
6.7%
2141.4691151
6.7%
739.7979251
6.7%
243.87317631
6.7%
96.027343781
6.7%
28.00986751
6.7%
20.410307291
6.7%
9.35481
6.7%
1.61
6.7%
1.51
6.7%

arsenicals_tonnes
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)30.8%
Missing2
Missing (%)13.3%
Memory size248.0 B
0.0
74.44
51.9
0.011

Length

Max length5
Median length3
Mean length3.615384615
Min length3

Characters and Unicode

Total characters47
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)7.7%

Sample

1st row0.0
2nd row0.0
3rd row74.44
4th row0.011
5th row51.9

Common Values

ValueCountFrequency (%)
0.08
53.3%
74.442
 
13.3%
51.92
 
13.3%
0.0111
 
6.7%
(Missing)2
 
13.3%

Length

2023-01-19T12:10:12.492228image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-19T12:10:12.688229image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.08
61.5%
74.442
 
15.4%
51.92
 
15.4%
0.0111
 
7.7%

Most occurring characters

ValueCountFrequency (%)
018
38.3%
.13
27.7%
46
 
12.8%
14
 
8.5%
72
 
4.3%
52
 
4.3%
92
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number34
72.3%
Other Punctuation13
 
27.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
018
52.9%
46
 
17.6%
14
 
11.8%
72
 
5.9%
52
 
5.9%
92
 
5.9%
Other Punctuation
ValueCountFrequency (%)
.13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common47
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
018
38.3%
.13
27.7%
46
 
12.8%
14
 
8.5%
72
 
4.3%
52
 
4.3%
92
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII47
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
018
38.3%
.13
27.7%
46
 
12.8%
14
 
8.5%
72
 
4.3%
52
 
4.3%
92
 
4.3%

cephalosporins__all_generations_tonnes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct10
Distinct (%)76.9%
Missing2
Missing (%)13.3%
Infinite0
Infinite (%)0.0%
Mean73.16635219
Minimum0
Maximum421.6469074
Zeros4
Zeros (%)26.7%
Negative0
Negative (%)0.0%
Memory size248.0 B
2023-01-19T12:10:12.793679image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.752115809
Q349.93894543
95-th percentile352.6165799
Maximum421.6469074
Range421.6469074
Interquartile range (IQR)49.93894543

Descriptive statistics

Standard deviation135.4947452
Coefficient of variation (CV)1.851872359
Kurtosis3.40928203
Mean73.16635219
Median Absolute Deviation (MAD)1.752115809
Skewness2.063558357
Sum951.1625784
Variance18358.82599
MonotonicityNot monotonic
2023-01-19T12:10:12.910681image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
04
26.7%
10.01789961
 
6.7%
116.27410461
 
6.7%
421.64690741
 
6.7%
44.660103951
 
6.7%
0.21
 
6.7%
0.076141
 
6.7%
49.938945431
 
6.7%
306.59636171
 
6.7%
1.7521158091
 
6.7%
(Missing)2
13.3%
ValueCountFrequency (%)
04
26.7%
0.076141
 
6.7%
0.21
 
6.7%
1.7521158091
 
6.7%
10.01789961
 
6.7%
44.660103951
 
6.7%
49.938945431
 
6.7%
116.27410461
 
6.7%
306.59636171
 
6.7%
421.64690741
 
6.7%
ValueCountFrequency (%)
421.64690741
 
6.7%
306.59636171
 
6.7%
116.27410461
 
6.7%
49.938945431
 
6.7%
44.660103951
 
6.7%
10.01789961
 
6.7%
1.7521158091
 
6.7%
0.21
 
6.7%
0.076141
 
6.7%
04
26.7%

1_2_gen__cephalosporins_tonnes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct9
Distinct (%)69.2%
Missing2
Missing (%)13.3%
Infinite0
Infinite (%)0.0%
Mean13.1849347
Minimum0
Maximum55.61275646
Zeros5
Zeros (%)33.3%
Negative0
Negative (%)0.0%
Memory size248.0 B
2023-01-19T12:10:13.040680image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.02649
Q325.71769227
95-th percentile48.1501622
Maximum55.61275646
Range55.61275646
Interquartile range (IQR)25.71769227

Descriptive statistics

Standard deviation19.16804799
Coefficient of variation (CV)1.453784067
Kurtosis0.505232692
Mean13.1849347
Median Absolute Deviation (MAD)0.02649
Skewness1.270684416
Sum171.4041511
Variance367.4140637
MonotonicityNot monotonic
2023-01-19T12:10:13.151683image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
05
33.3%
0.026491
 
6.7%
55.612756461
 
6.7%
27.308859371
 
6.7%
25.717692271
 
6.7%
0.0021
 
6.7%
43.175099361
 
6.7%
17.315807411
 
6.7%
2.24544621
 
6.7%
(Missing)2
 
13.3%
ValueCountFrequency (%)
05
33.3%
0.0021
 
6.7%
0.026491
 
6.7%
2.24544621
 
6.7%
17.315807411
 
6.7%
25.717692271
 
6.7%
27.308859371
 
6.7%
43.175099361
 
6.7%
55.612756461
 
6.7%
ValueCountFrequency (%)
55.612756461
 
6.7%
43.175099361
 
6.7%
27.308859371
 
6.7%
25.717692271
 
6.7%
17.315807411
 
6.7%
2.24544621
 
6.7%
0.026491
 
6.7%
0.0021
 
6.7%
05
33.3%

3_4_gen_cephalosporins_tonnes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct9
Distinct (%)69.2%
Missing2
Missing (%)13.3%
Infinite0
Infinite (%)0.0%
Mean49.47783667
Minimum0
Maximum303.072048
Zeros5
Zeros (%)33.3%
Negative0
Negative (%)0.0%
Memory size248.0 B
2023-01-19T12:10:13.287082image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.0874096
Q317.56630867
95-th percentile294.7971517
Maximum303.072048
Range303.072048
Interquartile range (IQR)17.56630867

Descriptive statistics

Standard deviation109.8133531
Coefficient of variation (CV)2.219445322
Kurtosis3.178847201
Mean49.47783667
Median Absolute Deviation (MAD)0.0874096
Skewness2.157785512
Sum643.2118767
Variance12058.97253
MonotonicityNot monotonic
2023-01-19T12:10:13.403045image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
05
33.3%
0.08740961
 
6.7%
25.316200491
 
6.7%
303.0720481
 
6.7%
17.566308671
 
6.7%
0.000341
 
6.7%
6.7628460661
 
6.7%
289.28055421
 
6.7%
1.1261696091
 
6.7%
(Missing)2
 
13.3%
ValueCountFrequency (%)
05
33.3%
0.000341
 
6.7%
0.08740961
 
6.7%
1.1261696091
 
6.7%
6.7628460661
 
6.7%
17.566308671
 
6.7%
25.316200491
 
6.7%
289.28055421
 
6.7%
303.0720481
 
6.7%
ValueCountFrequency (%)
303.0720481
 
6.7%
289.28055421
 
6.7%
25.316200491
 
6.7%
17.566308671
 
6.7%
6.7628460661
 
6.7%
1.1261696091
 
6.7%
0.08740961
 
6.7%
0.000341
 
6.7%
05
33.3%

fluoroquinolones_tonnes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct11
Distinct (%)84.6%
Missing2
Missing (%)13.3%
Infinite0
Infinite (%)0.0%
Mean175.4904353
Minimum0
Maximum867.5345785
Zeros3
Zeros (%)20.0%
Negative0
Negative (%)0.0%
Memory size248.0 B
2023-01-19T12:10:13.546085image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.16
median21.7742
Q3219.0016683
95-th percentile631.832986
Maximum867.5345785
Range867.5345785
Interquartile range (IQR)218.8416683

Descriptive statistics

Standard deviation265.8389248
Coefficient of variation (CV)1.514834266
Kurtosis2.910419822
Mean175.4904353
Median Absolute Deviation (MAD)21.7742
Skewness1.773605196
Sum2281.375659
Variance70670.33394
MonotonicityNot monotonic
2023-01-19T12:10:13.662042image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
03
20.0%
91.397797911
 
6.7%
428.54548521
 
6.7%
867.53457851
 
6.7%
219.00166831
 
6.7%
0.7291
 
6.7%
21.77421
 
6.7%
173.17156191
 
6.7%
474.6985911
 
6.7%
4.3627758621
 
6.7%
(Missing)2
13.3%
ValueCountFrequency (%)
03
20.0%
0.161
 
6.7%
0.7291
 
6.7%
4.3627758621
 
6.7%
21.77421
 
6.7%
91.397797911
 
6.7%
173.17156191
 
6.7%
219.00166831
 
6.7%
428.54548521
 
6.7%
474.6985911
 
6.7%
ValueCountFrequency (%)
867.53457851
6.7%
474.6985911
6.7%
428.54548521
6.7%
219.00166831
6.7%
173.17156191
6.7%
91.397797911
6.7%
21.77421
6.7%
4.3627758621
6.7%
0.7291
6.7%
0.161
6.7%

glycopeptides_tonnes
Categorical

HIGH CORRELATION
MISSING

Distinct5
Distinct (%)38.5%
Missing2
Missing (%)13.3%
Memory size248.0 B
0.0
1.0
0.503
0.345
0.255

Length

Max length5
Median length3
Mean length3.461538462
Min length3

Characters and Unicode

Total characters45
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)30.8%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.09
60.0%
1.01
 
6.7%
0.5031
 
6.7%
0.3451
 
6.7%
0.2551
 
6.7%
(Missing)2
 
13.3%

Length

2023-01-19T12:10:13.818380image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-19T12:10:14.002706image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.09
69.2%
1.01
 
7.7%
0.5031
 
7.7%
0.3451
 
7.7%
0.2551
 
7.7%

Most occurring characters

ValueCountFrequency (%)
023
51.1%
.13
28.9%
54
 
8.9%
32
 
4.4%
11
 
2.2%
41
 
2.2%
21
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number32
71.1%
Other Punctuation13
28.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
023
71.9%
54
 
12.5%
32
 
6.2%
11
 
3.1%
41
 
3.1%
21
 
3.1%
Other Punctuation
ValueCountFrequency (%)
.13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common45
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
023
51.1%
.13
28.9%
54
 
8.9%
32
 
4.4%
11
 
2.2%
41
 
2.2%
21
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII45
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
023
51.1%
.13
28.9%
54
 
8.9%
32
 
4.4%
11
 
2.2%
41
 
2.2%
21
 
2.2%

glycophospholipids_tonnes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct7
Distinct (%)53.8%
Missing2
Missing (%)13.3%
Infinite0
Infinite (%)0.0%
Mean35.77978566
Minimum0
Maximum110
Zeros6
Zeros (%)40.0%
Negative0
Negative (%)0.0%
Memory size248.0 B
2023-01-19T12:10:14.106844image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.084
Q397.183
95-th percentile110
Maximum110
Range110
Interquartile range (IQR)97.183

Descriptive statistics

Standard deviation48.52181089
Coefficient of variation (CV)1.356123576
Kurtosis-1.36942214
Mean35.77978566
Median Absolute Deviation (MAD)0.084
Skewness0.8399107686
Sum465.1372136
Variance2354.366132
MonotonicityNot monotonic
2023-01-19T12:10:14.221161image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
06
40.0%
1102
 
13.3%
13.907806781
 
6.7%
97.1831
 
6.7%
34.701406781
 
6.7%
99.2611
 
6.7%
0.0841
 
6.7%
(Missing)2
 
13.3%
ValueCountFrequency (%)
06
40.0%
0.0841
 
6.7%
13.907806781
 
6.7%
34.701406781
 
6.7%
97.1831
 
6.7%
99.2611
 
6.7%
1102
 
13.3%
ValueCountFrequency (%)
1102
 
13.3%
99.2611
 
6.7%
97.1831
 
6.7%
34.701406781
 
6.7%
13.907806781
 
6.7%
0.0841
 
6.7%
06
40.0%

lincosamides_tonnes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct12
Distinct (%)92.3%
Missing2
Missing (%)13.3%
Infinite0
Infinite (%)0.0%
Mean146.5878759
Minimum0
Maximum647.3119386
Zeros2
Zeros (%)13.3%
Negative0
Negative (%)0.0%
Memory size248.0 B
2023-01-19T12:10:14.362498image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.06
median10.19814494
Q3303.6764393
95-th percentile553.4903228
Maximum647.3119386
Range647.3119386
Interquartile range (IQR)303.6164393

Descriptive statistics

Standard deviation227.3344375
Coefficient of variation (CV)1.550840655
Kurtosis0.4042254992
Mean146.5878759
Median Absolute Deviation (MAD)10.19814494
Skewness1.330786063
Sum1905.642387
Variance51680.94647
MonotonicityNot monotonic
2023-01-19T12:10:14.480541image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
02
13.3%
22.02107821
6.7%
0.211
6.7%
1.364531
6.7%
379.67399611
6.7%
647.31193861
6.7%
303.67643931
6.7%
0.061
6.7%
0.02891
6.7%
50.154780391
6.7%
Other values (2)2
13.3%
(Missing)2
13.3%
ValueCountFrequency (%)
02
13.3%
0.02891
6.7%
0.061
6.7%
0.211
6.7%
1.364531
6.7%
10.198144941
6.7%
22.02107821
6.7%
50.154780391
6.7%
303.67643931
6.7%
379.67399611
6.7%
ValueCountFrequency (%)
647.31193861
6.7%
490.9425791
6.7%
379.67399611
6.7%
303.67643931
6.7%
50.154780391
6.7%
22.02107821
6.7%
10.198144941
6.7%
1.364531
6.7%
0.211
6.7%
0.061
6.7%

macrolides_tonnes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct13
Distinct (%)100.0%
Missing2
Missing (%)13.3%
Infinite0
Infinite (%)0.0%
Mean760.3804033
Minimum0
Maximum3981.087715
Zeros1
Zeros (%)6.7%
Negative0
Negative (%)0.0%
Memory size248.0 B
2023-01-19T12:10:14.624500image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.674
Q125.8593
median78.34148013
Q31174.582
95-th percentile2871.066891
Maximum3981.087715
Range3981.087715
Interquartile range (IQR)1148.7227

Descriptive statistics

Standard deviation1185.042827
Coefficient of variation (CV)1.558486807
Kurtosis4.001273457
Mean760.3804033
Median Absolute Deviation (MAD)78.34148013
Skewness1.99084562
Sum9884.945243
Variance1404326.502
MonotonicityNot monotonic
2023-01-19T12:10:14.756639image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
01
 
6.7%
62.463051991
 
6.7%
1174.5821
 
6.7%
78.341480131
 
6.7%
1472.3875461
 
6.7%
3981.0877151
 
6.7%
565.37699241
 
6.7%
8.011
 
6.7%
25.85931
 
6.7%
307.94853991
 
6.7%
Other values (3)3
20.0%
(Missing)2
13.3%
ValueCountFrequency (%)
01
6.7%
7.791
6.7%
8.011
6.7%
25.85931
6.7%
62.463051991
6.7%
70.045609751
6.7%
78.341480131
6.7%
307.94853991
6.7%
565.37699241
6.7%
1174.5821
6.7%
ValueCountFrequency (%)
3981.0877151
6.7%
2131.0530081
6.7%
1472.3875461
6.7%
1174.5821
6.7%
565.37699241
6.7%
307.94853991
6.7%
78.341480131
6.7%
70.045609751
6.7%
62.463051991
6.7%
25.85931
6.7%

nitrofurans_tonnes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct8
Distinct (%)61.5%
Missing2
Missing (%)13.3%
Infinite0
Infinite (%)0.0%
Mean0.326421156
Minimum0
Maximum3.2116587
Zeros6
Zeros (%)40.0%
Negative0
Negative (%)0.0%
Memory size248.0 B
2023-01-19T12:10:14.908748image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.001
Q30.16666
95-th percentile1.53231096
Maximum3.2116587
Range3.2116587
Interquartile range (IQR)0.16666

Descriptive statistics

Standard deviation0.8774455909
Coefficient of variation (CV)2.688078192
Kurtosis12.21315084
Mean0.326421156
Median Absolute Deviation (MAD)0.001
Skewness3.4599527
Sum4.243475028
Variance0.7699107651
MonotonicityNot monotonic
2023-01-19T12:10:15.023176image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
06
40.0%
0.166661
 
6.7%
0.3034105281
 
6.7%
3.21165871
 
6.7%
0.41274581
 
6.7%
0.011
 
6.7%
0.1381
 
6.7%
0.0011
 
6.7%
(Missing)2
 
13.3%
ValueCountFrequency (%)
06
40.0%
0.0011
 
6.7%
0.011
 
6.7%
0.1381
 
6.7%
0.166661
 
6.7%
0.3034105281
 
6.7%
0.41274581
 
6.7%
3.21165871
 
6.7%
ValueCountFrequency (%)
3.21165871
 
6.7%
0.41274581
 
6.7%
0.3034105281
 
6.7%
0.166661
 
6.7%
0.1381
 
6.7%
0.011
 
6.7%
0.0011
 
6.7%
06
40.0%

orthosomycins_tonnes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct8
Distinct (%)61.5%
Missing2
Missing (%)13.3%
Infinite0
Infinite (%)0.0%
Mean9.643115023
Minimum0
Maximum40.708
Zeros6
Zeros (%)40.0%
Negative0
Negative (%)0.0%
Memory size248.0 B
2023-01-19T12:10:15.159256image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3.353
Q318.715525
95-th percentile33.04344859
Maximum40.708
Range40.708
Interquartile range (IQR)18.715525

Descriptive statistics

Standard deviation13.88251932
Coefficient of variation (CV)1.43963017
Kurtosis0.4542383464
Mean9.643115023
Median Absolute Deviation (MAD)3.353
Skewness1.303843008
Sum125.3604953
Variance192.7243426
MonotonicityNot monotonic
2023-01-19T12:10:15.275213image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
06
40.0%
3.642222651
 
6.7%
26.1951
 
6.7%
27.933747651
 
6.7%
40.7081
 
6.7%
4.8131
 
6.7%
18.7155251
 
6.7%
3.3531
 
6.7%
(Missing)2
 
13.3%
ValueCountFrequency (%)
06
40.0%
3.3531
 
6.7%
3.642222651
 
6.7%
4.8131
 
6.7%
18.7155251
 
6.7%
26.1951
 
6.7%
27.933747651
 
6.7%
40.7081
 
6.7%
ValueCountFrequency (%)
40.7081
 
6.7%
27.933747651
 
6.7%
26.1951
 
6.7%
18.7155251
 
6.7%
4.8131
 
6.7%
3.642222651
 
6.7%
3.3531
 
6.7%
06
40.0%

other_quinolones_tonnes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct9
Distinct (%)69.2%
Missing2
Missing (%)13.3%
Infinite0
Infinite (%)0.0%
Mean4.277799327
Minimum0
Maximum20.504566
Zeros5
Zeros (%)33.3%
Negative0
Negative (%)0.0%
Memory size248.0 B
2023-01-19T12:10:15.419389image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.1
Q33.9355
95-th percentile19.56556675
Maximum20.504566
Range20.504566
Interquartile range (IQR)3.9355

Descriptive statistics

Standard deviation7.257159324
Coefficient of variation (CV)1.696470257
Kurtosis1.946834055
Mean4.277799327
Median Absolute Deviation (MAD)0.1
Skewness1.776893607
Sum55.61139125
Variance52.66636146
MonotonicityNot monotonic
2023-01-19T12:10:15.541305image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
05
33.3%
0.839291
 
6.7%
8.0160181
 
6.7%
20.5045661
 
6.7%
18.939567251
 
6.7%
0.11
 
6.7%
0.069851
 
6.7%
3.93551
 
6.7%
3.20661
 
6.7%
(Missing)2
 
13.3%
ValueCountFrequency (%)
05
33.3%
0.069851
 
6.7%
0.11
 
6.7%
0.839291
 
6.7%
3.20661
 
6.7%
3.93551
 
6.7%
8.0160181
 
6.7%
18.939567251
 
6.7%
20.5045661
 
6.7%
ValueCountFrequency (%)
20.5045661
 
6.7%
18.939567251
 
6.7%
8.0160181
 
6.7%
3.93551
 
6.7%
3.20661
 
6.7%
0.839291
 
6.7%
0.11
 
6.7%
0.069851
 
6.7%
05
33.3%

penicillins_tonnes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct13
Distinct (%)100.0%
Missing2
Missing (%)13.3%
Infinite0
Infinite (%)0.0%
Mean1069.321626
Minimum0
Maximum5809.31073
Zeros1
Zeros (%)6.7%
Negative0
Negative (%)0.0%
Memory size248.0 B
2023-01-19T12:10:15.676603image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.015
Q14.881
median139.483171
Q31851.932388
95-th percentile4421.436841
Maximum5809.31073
Range5809.31073
Interquartile range (IQR)1847.051388

Descriptive statistics

Standard deviation1793.298079
Coefficient of variation (CV)1.67704275
Kurtosis3.400307725
Mean1069.321626
Median Absolute Deviation (MAD)139.458171
Skewness1.930032604
Sum13901.18113
Variance3215918.001
MonotonicityNot monotonic
2023-01-19T12:10:15.792065image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
01
 
6.7%
0.0251
 
6.7%
7.1741
 
6.7%
139.4831711
 
6.7%
1851.9323881
 
6.7%
5809.310731
 
6.7%
1957.1912461
 
6.7%
4.8811
 
6.7%
23.968692351
 
6.7%
401.54056691
 
6.7%
Other values (3)3
20.0%
(Missing)2
13.3%
ValueCountFrequency (%)
01
6.7%
0.0251
6.7%
2.5251
6.7%
4.8811
6.7%
7.1741
6.7%
23.968692351
6.7%
139.4831711
6.7%
206.96175721
6.7%
401.54056691
6.7%
1851.9323881
6.7%
ValueCountFrequency (%)
5809.310731
6.7%
3496.1875821
6.7%
1957.1912461
6.7%
1851.9323881
6.7%
401.54056691
6.7%
206.96175721
6.7%
139.4831711
6.7%
23.968692351
6.7%
7.1741
6.7%
4.8811
6.7%

pleuromutilins_tonnes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct10
Distinct (%)76.9%
Missing2
Missing (%)13.3%
Infinite0
Infinite (%)0.0%
Mean204.6781319
Minimum0
Maximum1161.891315
Zeros4
Zeros (%)26.7%
Negative0
Negative (%)0.0%
Memory size248.0 B
2023-01-19T12:10:15.926260image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median15.7585171
Q3225.3743949
95-th percentile848.5778435
Maximum1161.891315
Range1161.891315
Interquartile range (IQR)225.3743949

Descriptive statistics

Standard deviation346.6248646
Coefficient of variation (CV)1.693511961
Kurtosis4.555674791
Mean204.6781319
Median Absolute Deviation (MAD)15.7585171
Skewness2.135356394
Sum2660.815715
Variance120148.7968
MonotonicityNot monotonic
2023-01-19T12:10:16.047605image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
04
26.7%
37.982856871
 
6.7%
12.3991
 
6.7%
3.61
 
6.7%
369.87764251
 
6.7%
1161.8913151
 
6.7%
194.22979321
 
6.7%
225.37439491
 
6.7%
639.70219591
 
6.7%
15.75851711
 
6.7%
(Missing)2
13.3%
ValueCountFrequency (%)
04
26.7%
3.61
 
6.7%
12.3991
 
6.7%
15.75851711
 
6.7%
37.982856871
 
6.7%
194.22979321
 
6.7%
225.37439491
 
6.7%
369.87764251
 
6.7%
639.70219591
 
6.7%
1161.8913151
 
6.7%
ValueCountFrequency (%)
1161.8913151
 
6.7%
639.70219591
 
6.7%
369.87764251
 
6.7%
225.37439491
 
6.7%
194.22979321
 
6.7%
37.982856871
 
6.7%
15.75851711
 
6.7%
12.3991
 
6.7%
3.61
 
6.7%
04
26.7%

polypeptides_tonnes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct13
Distinct (%)100.0%
Missing2
Missing (%)13.3%
Infinite0
Infinite (%)0.0%
Mean596.1801997
Minimum0
Maximum3055.98336
Zeros1
Zeros (%)6.7%
Negative0
Negative (%)0.0%
Memory size248.0 B
2023-01-19T12:10:16.196737image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.03
Q18.007412247
median169.3996694
Q3502.087349
95-th percentile2270.589757
Maximum3055.98336
Range3055.98336
Interquartile range (IQR)494.0799367

Descriptive statistics

Standard deviation939.9529373
Coefficient of variation (CV)1.576625553
Kurtosis3.106691695
Mean596.1801997
Median Absolute Deviation (MAD)169.3496694
Skewness1.881214376
Sum7750.342596
Variance883511.5243
MonotonicityNot monotonic
2023-01-19T12:10:16.359186image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0.1341
 
6.7%
502.0873491
 
6.7%
1514.7581
 
6.7%
50.477093851
 
6.7%
1746.9940221
 
6.7%
3055.983361
 
6.7%
234.45246841
 
6.7%
0.051
 
6.7%
8.0074122471
 
6.7%
169.39966941
 
6.7%
Other values (3)3
20.0%
(Missing)2
13.3%
ValueCountFrequency (%)
01
6.7%
0.051
6.7%
0.1341
6.7%
8.0074122471
6.7%
22.640610551
6.7%
50.477093851
6.7%
169.39966941
6.7%
234.45246841
6.7%
445.35861041
6.7%
502.0873491
6.7%
ValueCountFrequency (%)
3055.983361
6.7%
1746.9940221
6.7%
1514.7581
6.7%
502.0873491
6.7%
445.35861041
6.7%
234.45246841
6.7%
169.39966941
6.7%
50.477093851
6.7%
22.640610551
6.7%
8.0074122471
6.7%

quinoxalines_tonnes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct6
Distinct (%)46.2%
Missing2
Missing (%)13.3%
Infinite0
Infinite (%)0.0%
Mean208.7578462
Minimum0
Maximum1346.323
Zeros8
Zeros (%)53.3%
Negative0
Negative (%)0.0%
Memory size248.0 B
2023-01-19T12:10:16.503384image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q320.372
95-th percentile1325.857
Maximum1346.323
Range1346.323
Interquartile range (IQR)20.372

Descriptive statistics

Standard deviation497.4582347
Coefficient of variation (CV)2.382943893
Kurtosis3.224424654
Mean208.7578462
Median Absolute Deviation (MAD)0
Skewness2.177522672
Sum2713.852
Variance247464.6952
MonotonicityNot monotonic
2023-01-19T12:10:16.623516image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
08
53.3%
1312.2131
 
6.7%
34.7941
 
6.7%
1346.3231
 
6.7%
0.151
 
6.7%
20.3721
 
6.7%
(Missing)2
 
13.3%
ValueCountFrequency (%)
08
53.3%
0.151
 
6.7%
20.3721
 
6.7%
34.7941
 
6.7%
1312.2131
 
6.7%
1346.3231
 
6.7%
ValueCountFrequency (%)
1346.3231
 
6.7%
1312.2131
 
6.7%
34.7941
 
6.7%
20.3721
 
6.7%
0.151
 
6.7%
08
53.3%

streptogramins_tonnes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct10
Distinct (%)76.9%
Missing2
Missing (%)13.3%
Infinite0
Infinite (%)0.0%
Mean74.18255024
Minimum0
Maximum473.259
Zeros4
Zeros (%)26.7%
Negative0
Negative (%)0.0%
Memory size248.0 B
2023-01-19T12:10:16.758519image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.272
Q35.06369
95-th percentile468.1686
Maximum473.259
Range473.259
Interquartile range (IQR)5.06369

Descriptive statistics

Standard deviation175.2858013
Coefficient of variation (CV)2.362898023
Kurtosis3.220157481
Mean74.18255024
Median Absolute Deviation (MAD)0.272
Skewness2.176798213
Sum964.3731531
Variance30725.11214
MonotonicityNot monotonic
2023-01-19T12:10:16.876703image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
04
26.7%
4.862386531
 
6.7%
464.7751
 
6.7%
0.0041
 
6.7%
14.067076531
 
6.7%
473.2591
 
6.7%
1.9681
 
6.7%
0.1021
 
6.7%
5.063691
 
6.7%
0.2721
 
6.7%
(Missing)2
13.3%
ValueCountFrequency (%)
04
26.7%
0.0041
 
6.7%
0.1021
 
6.7%
0.2721
 
6.7%
1.9681
 
6.7%
4.862386531
 
6.7%
5.063691
 
6.7%
14.067076531
 
6.7%
464.7751
 
6.7%
473.2591
 
6.7%
ValueCountFrequency (%)
473.2591
 
6.7%
464.7751
 
6.7%
14.067076531
 
6.7%
5.063691
 
6.7%
4.862386531
 
6.7%
1.9681
 
6.7%
0.2721
 
6.7%
0.1021
 
6.7%
0.0041
 
6.7%
04
26.7%

sulfonamides__including_trimethoprim_tonnes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct13
Distinct (%)100.0%
Missing2
Missing (%)13.3%
Infinite0
Infinite (%)0.0%
Mean405.5551322
Minimum0
Maximum1892.112046
Zeros1
Zeros (%)6.7%
Negative0
Negative (%)0.0%
Memory size248.0 B
2023-01-19T12:10:17.024873image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.462
Q12.245
median103.4303775
Q3741.2669018
95-th percentile1577.52616
Maximum1892.112046
Range1892.112046
Interquartile range (IQR)739.0219018

Descriptive statistics

Standard deviation616.5296625
Coefficient of variation (CV)1.520211714
Kurtosis1.744417239
Mean405.5551322
Median Absolute Deviation (MAD)102.3053775
Skewness1.61305708
Sum5272.216719
Variance380108.8247
MonotonicityNot monotonic
2023-01-19T12:10:17.150032image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
3.8631
 
6.7%
01
 
6.7%
0.771
 
6.7%
103.43037751
 
6.7%
796.41454531
 
6.7%
1892.1120461
 
6.7%
741.26690181
 
6.7%
2.2451
 
6.7%
39.248080511
 
6.7%
158.21083041
 
6.7%
Other values (3)3
20.0%
(Missing)2
13.3%
ValueCountFrequency (%)
01
6.7%
0.771
6.7%
1.1251
6.7%
2.2451
6.7%
3.8631
6.7%
39.248080511
6.7%
103.43037751
6.7%
158.21083041
6.7%
165.72870111
6.7%
741.26690181
6.7%
ValueCountFrequency (%)
1892.1120461
6.7%
1367.8022371
6.7%
796.41454531
6.7%
741.26690181
6.7%
165.72870111
6.7%
158.21083041
6.7%
103.43037751
6.7%
39.248080511
6.7%
3.8631
6.7%
2.2451
6.7%

tetracyclines_tonnes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct13
Distinct (%)100.0%
Missing2
Missing (%)13.3%
Infinite0
Infinite (%)0.0%
Mean3403.409211
Minimum3.902
Maximum16562.58734
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size248.0 B
2023-01-19T12:10:17.339780image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum3.902
5-th percentile4.44840734
Q110.79
median548.0339444
Q32778.476279
95-th percentile13995.83633
Maximum16562.58734
Range16558.68534
Interquartile range (IQR)2767.686279

Descriptive statistics

Standard deviation5487.09672
Coefficient of variation (CV)1.612235373
Kurtosis1.813938781
Mean3403.409211
Median Absolute Deviation (MAD)543.2212655
Skewness1.698052112
Sum44244.31975
Variance30108230.42
MonotonicityNot monotonic
2023-01-19T12:10:17.465076image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
3.9021
 
6.7%
4.81267891
 
6.7%
12284.6691
 
6.7%
740.34804671
 
6.7%
8517.6928081
 
6.7%
16562.587341
 
6.7%
2320.5690421
 
6.7%
10.791
 
6.7%
99.316436831
 
6.7%
548.03394441
 
6.7%
Other values (3)3
20.0%
(Missing)2
13.3%
ValueCountFrequency (%)
3.9021
6.7%
4.81267891
6.7%
8.0231
6.7%
10.791
6.7%
99.316436831
6.7%
365.09917541
6.7%
548.03394441
6.7%
740.34804671
6.7%
2320.5690421
6.7%
2778.4762791
6.7%
ValueCountFrequency (%)
16562.587341
6.7%
12284.6691
6.7%
8517.6928081
6.7%
2778.4762791
6.7%
2320.5690421
6.7%
740.34804671
6.7%
548.03394441
6.7%
365.09917541
6.7%
99.316436831
6.7%
10.791
6.7%

others_tonnes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct11
Distinct (%)84.6%
Missing2
Missing (%)13.3%
Infinite0
Infinite (%)0.0%
Mean117.3016726
Minimum0
Maximum691.9261727
Zeros3
Zeros (%)20.0%
Negative0
Negative (%)0.0%
Memory size248.0 B
2023-01-19T12:10:17.625745image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.7595822
median23.377208
Q367.88025437
95-th percentile543.7673264
Maximum691.9261727
Range691.9261727
Interquartile range (IQR)67.12067217

Descriptive statistics

Standard deviation214.8329145
Coefficient of variation (CV)1.831456532
Kurtosis3.963988093
Mean117.3016726
Median Absolute Deviation (MAD)23.377208
Skewness2.1328394
Sum1524.921744
Variance46153.18114
MonotonicityNot monotonic
2023-01-19T12:10:17.748327image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
03
20.0%
19.251
 
6.7%
221.41781
 
6.7%
23.584684151
 
6.7%
444.99476221
 
6.7%
691.92617271
 
6.7%
67.880254371
 
6.7%
3.19681
 
6.7%
23.3772081
 
6.7%
28.534481
 
6.7%
(Missing)2
13.3%
ValueCountFrequency (%)
03
20.0%
0.75958221
 
6.7%
3.19681
 
6.7%
19.251
 
6.7%
23.3772081
 
6.7%
23.584684151
 
6.7%
28.534481
 
6.7%
67.880254371
 
6.7%
221.41781
 
6.7%
444.99476221
 
6.7%
ValueCountFrequency (%)
691.92617271
6.7%
444.99476221
6.7%
221.41781
6.7%
67.880254371
6.7%
28.534481
6.7%
23.584684151
6.7%
23.3772081
6.7%
19.251
6.7%
3.19681
6.7%
0.75958221
6.7%

aggregated_class_data_tonnes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct9
Distinct (%)69.2%
Missing2
Missing (%)13.3%
Infinite0
Infinite (%)0.0%
Mean151.5719188
Minimum0
Maximum1342.566904
Zeros4
Zeros (%)26.7%
Negative0
Negative (%)0.0%
Memory size248.0 B
2023-01-19T12:10:17.894218image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2.112
Q3136.05182
95-th percentile664.5663616
Maximum1342.566904
Range1342.566904
Interquartile range (IQR)136.05182

Descriptive statistics

Standard deviation365.9097415
Coefficient of variation (CV)2.414099818
Kurtosis11.57936955
Mean151.5719188
Median Absolute Deviation (MAD)2.112
Skewness3.341953919
Sum1970.434944
Variance133889.9389
MonotonicityNot monotonic
2023-01-19T12:10:18.019382image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
04
26.7%
2.1122
13.3%
212.5661
 
6.7%
1342.5669041
 
6.7%
177.27291
 
6.7%
10.41421
 
6.7%
87.284121
 
6.7%
136.051821
 
6.7%
0.0551
 
6.7%
(Missing)2
13.3%
ValueCountFrequency (%)
04
26.7%
0.0551
 
6.7%
2.1122
13.3%
10.41421
 
6.7%
87.284121
 
6.7%
136.051821
 
6.7%
177.27291
 
6.7%
212.5661
 
6.7%
1342.5669041
 
6.7%
ValueCountFrequency (%)
1342.5669041
 
6.7%
212.5661
 
6.7%
177.27291
 
6.7%
136.051821
 
6.7%
87.284121
 
6.7%
10.41421
 
6.7%
2.1122
13.3%
0.0551
 
6.7%
04
26.7%

Interactions

2023-01-19T12:10:02.543093image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:08:38.707105image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:08:43.129396image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:08:48.536500image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:08:53.405053image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:08:57.132690image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:09:00.955375image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:09:04.745243image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:09:07.988640image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:09:11.384973image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:09:15.064151image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:09:19.044381image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:09:23.689201image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:09:28.934707image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:09:32.806082image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:09:36.191060image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:09:39.943641image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:09:43.570808image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:09:48.117483image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:09:51.601799image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:09:55.222726image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:09:58.709939image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2023-01-19T12:08:39.098391image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:08:43.299774image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:08:48.725810image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:08:53.575668image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:08:57.309321image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:09:01.124534image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:09:04.899383image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:09:08.147025image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:09:11.546131image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:09:15.228404image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:09:19.201656image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:09:23.941992image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:09:29.089833image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:09:32.970087image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:09:36.350063image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:09:40.098742image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:09:43.742993image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:09:48.273487image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:09:51.768326image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:09:55.413726image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:09:58.867565image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:10:02.882592image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:08:39.318410image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:08:43.493004image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:08:49.312111image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:08:53.732709image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:08:57.493324image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:09:01.277882image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:09:05.043306image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:09:08.297376image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:09:11.697129image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:09:15.396447image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:09:19.387658image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:09:24.156338image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:09:29.263836image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:09:33.166125image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:09:36.511412image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:09:40.249916image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:09:43.912168image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:09:48.422653image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:09:51.940476image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:09:55.567888image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:09:59.103123image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:10:03.072772image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:08:39.517553image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:08:43.732403image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:08:49.603761image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:08:53.880670image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2023-01-19T12:09:08.434376image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2023-01-19T12:09:15.561613image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2023-01-19T12:09:24.374917image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:09:29.484455image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2023-01-19T12:09:40.391128image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2023-01-19T12:09:52.089778image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:09:55.711247image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:09:59.308729image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:10:03.231294image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:08:39.687484image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:08:43.885004image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:08:49.800333image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:08:54.016649image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:08:57.821743image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:09:01.558584image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:09:05.319941image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:09:08.572378image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:09:12.019646image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:09:15.711571image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:09:19.674923image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:09:24.584102image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:09:29.633450image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:09:33.496084image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:09:36.794146image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:09:40.645859image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:09:44.466401image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:09:48.727653image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:09:52.237943image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:09:55.921724image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:09:59.449875image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:10:03.489299image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:08:39.884481image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:08:44.041174image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:08:49.982334image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:08:54.159282image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:08:57.970540image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:09:01.706746image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:09:05.464882image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:09:08.717375image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:09:12.164824image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:09:15.863810image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:09:19.826133image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:09:24.791031image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:09:29.771801image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2023-01-19T12:09:36.936321image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2023-01-19T12:08:54.321284image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2023-01-19T12:08:50.504511image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:08:54.456283image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:08:58.250193image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2023-01-19T12:09:08.996756image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:09:12.449823image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:09:16.201972image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:09:20.305714image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-19T12:09:25.252940image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2023-01-19T12:10:02.389845image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2023-01-19T12:10:18.188048image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2023-01-19T12:10:18.707873image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2023-01-19T12:10:19.230685image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2023-01-19T12:10:19.710630image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2023-01-19T12:10:19.936979image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2023-01-19T12:10:06.696973image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-19T12:10:08.980546image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-01-19T12:10:09.641567image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2023-01-19T12:10:10.647493image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

scoperegionnumber_of_countriesaminoglycosides_tonnesamphenicols_tonnesarsenicals_tonnescephalosporins__all_generations_tonnes1_2_gen__cephalosporins_tonnes3_4_gen_cephalosporins_tonnesfluoroquinolones_tonnesglycopeptides_tonnesglycophospholipids_tonneslincosamides_tonnesmacrolides_tonnesnitrofurans_tonnesorthosomycins_tonnesother_quinolones_tonnespenicillins_tonnespleuromutilins_tonnespolypeptides_tonnesquinoxalines_tonnesstreptogramins_tonnessulfonamides__including_trimethoprim_tonnestetracyclines_tonnesothers_tonnesaggregated_class_data_tonnes
0AGPAfrica1.00.0000000.0000000.0000.0000000.0000000.0000000.0000000.0000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.1340000.0000.0000003.8630003.90200019.2500002.112000
1AGPAmericas5.00.0000000.0000000.0000.0000000.0000000.0000000.0000000.00013.90780722.02107862.4630520.0000003.6422230.0000000.02500037.982857502.0873490.0004.8623870.0000004.812679221.417800212.566000
2AGPAsia, Far East and Oceania6.00.7070000.00000074.4400.0000000.0000000.0000000.0000000.00097.1830000.2100001174.5820000.00000026.1950000.0000007.17400012.3990001514.7580001312.213464.7750000.77000012284.6690000.0000000.000000
3AGPEurope0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
4AGPMiddle East0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
5AllAfrica24.033.65154420.4103070.01110.0179000.0264900.08741091.3977981.000110.0000001.36453078.3414800.1666600.0000000.839290139.4831713.60000050.4770940.0000.004000103.430377740.34804723.5846842.112000
6AllAmericas19.0762.375783739.79792551.900116.27410555.61275625.316200428.5454850.00034.701407379.6739961472.3875460.30341127.9337488.0160181851.932388369.8776421746.99402234.79414.067077796.4145458517.692808444.9947621342.566904
7AllAsia, Far East and Oceania22.01425.6633332422.62492474.440421.64690727.308859303.072048867.5345780.00099.261000647.3119393981.0877153.21165940.70800020.5045665809.3107301161.8913153055.9833601346.323473.2590001892.11204616562.587336691.926173177.272900
8AllEurope41.0527.643318243.8731760.00044.66010425.71769217.566309219.0016680.5030.084000303.676439565.3769920.4127464.81300018.9395671957.191246194.229793234.4524680.0001.968000741.2669022320.56904267.88025410.414200
9AllMiddle East3.04.6510001.6000000.0000.2000000.0000000.0000000.7290000.3450.0000000.0600008.0100000.0100000.0000000.1000004.8810000.0000000.0500000.1500.1020002.24500010.7900000.0000000.000000

Last rows

scoperegionnumber_of_countriesaminoglycosides_tonnesamphenicols_tonnesarsenicals_tonnescephalosporins__all_generations_tonnes1_2_gen__cephalosporins_tonnes3_4_gen_cephalosporins_tonnesfluoroquinolones_tonnesglycopeptides_tonnesglycophospholipids_tonneslincosamides_tonnesmacrolides_tonnesnitrofurans_tonnesorthosomycins_tonnesother_quinolones_tonnespenicillins_tonnespleuromutilins_tonnespolypeptides_tonnesquinoxalines_tonnesstreptogramins_tonnessulfonamides__including_trimethoprim_tonnestetracyclines_tonnesothers_tonnesaggregated_class_data_tonnes
5AllAfrica24.033.65154420.4103070.01110.0179000.0264900.08741091.3977981.000110.0000001.36453078.3414800.1666600.0000000.839290139.4831713.60000050.4770940.0000.004000103.430377740.34804723.5846842.112000
6AllAmericas19.0762.375783739.79792551.900116.27410555.61275625.316200428.5454850.00034.701407379.6739961472.3875460.30341127.9337488.0160181851.932388369.8776421746.99402234.79414.067077796.4145458517.692808444.9947621342.566904
7AllAsia, Far East and Oceania22.01425.6633332422.62492474.440421.64690727.308859303.072048867.5345780.00099.261000647.3119393981.0877153.21165940.70800020.5045665809.3107301161.8913153055.9833601346.323473.2590001892.11204616562.587336691.926173177.272900
8AllEurope41.0527.643318243.8731760.00044.66010425.71769217.566309219.0016680.5030.084000303.676439565.3769920.4127464.81300018.9395671957.191246194.229793234.4524680.0001.968000741.2669022320.56904267.88025410.414200
9AllMiddle East3.04.6510001.6000000.0000.2000000.0000000.0000000.7290000.3450.0000000.0600008.0100000.0100000.0000000.1000004.8810000.0000000.0500000.1500.1020002.24500010.7900000.0000000.000000
10Terrestrial Food ProducingAfrica6.03.8016869.3548000.0000.0761400.0020000.00034021.7742000.000110.0000000.02890025.8593000.1380000.0000000.06985023.9686920.0000008.0074120.0000.00000039.24808199.3164373.1968000.000000
11Terrestrial Food ProducingAmericas11.0166.50740496.02734451.90049.93894543.1750996.762846173.1715620.0000.00000050.154780307.9485400.00000018.7155250.000000401.540567225.374395169.3996690.0005.063690158.210830548.03394423.37720887.284120
12Terrestrial Food ProducingAsia, Far East and Oceania9.01210.6992132141.4691150.000306.59636217.315807289.280554474.6985910.0000.000000490.9425792131.0530080.0000003.3530003.9355003496.187582639.702196445.35861020.3720.2720001367.8022372778.47627928.534480136.051820
13Terrestrial Food ProducingEurope10.081.43779228.0098670.0001.7521162.2454461.1261704.3627760.0000.00000010.19814570.0456100.0010000.0000003.206600206.96175715.75851722.6406110.0000.000000165.728701365.0991750.7595820.055000
14Terrestrial Food ProducingMiddle East1.02.9700001.5000000.0000.0000000.0000000.0000000.1600000.2550.0000000.0000007.7900000.0000000.0000000.0000002.5250000.0000000.0000000.0000.0000001.1250008.0230000.0000000.000000